CN117075500A - Intelligent kitchen electricity self-adaptive control method and device - Google Patents
Intelligent kitchen electricity self-adaptive control method and device Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 62
- 230000005611 electricity Effects 0.000 title claims abstract description 23
- 238000010411 cooking Methods 0.000 claims abstract description 509
- 230000003044 adaptive effect Effects 0.000 claims abstract description 42
- 230000008569 process Effects 0.000 claims abstract description 37
- 230000009466 transformation Effects 0.000 claims abstract description 26
- 239000013598 vector Substances 0.000 claims description 28
- 230000008859 change Effects 0.000 claims description 25
- 238000006243 chemical reaction Methods 0.000 claims description 25
- 230000006399 behavior Effects 0.000 claims description 23
- 238000012360 testing method Methods 0.000 claims description 22
- 238000006073 displacement reaction Methods 0.000 claims description 16
- 230000001276 controlling effect Effects 0.000 claims description 5
- 230000003247 decreasing effect Effects 0.000 claims description 5
- 230000010363 phase shift Effects 0.000 claims description 5
- 230000001105 regulatory effect Effects 0.000 claims description 4
- 238000012216 screening Methods 0.000 claims description 4
- 238000011156 evaluation Methods 0.000 claims description 3
- 230000009286 beneficial effect Effects 0.000 description 8
- 238000010438 heat treatment Methods 0.000 description 8
- 239000007788 liquid Substances 0.000 description 6
- 238000011161 development Methods 0.000 description 4
- 230000004069 differentiation Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 239000004278 EU approved seasoning Substances 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 235000013410 fast food Nutrition 0.000 description 1
- 235000013305 food Nutrition 0.000 description 1
- 235000011194 food seasoning agent Nutrition 0.000 description 1
- 239000003205 fragrance Substances 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 230000035484 reaction time Effects 0.000 description 1
- 230000000630 rising effect Effects 0.000 description 1
- 238000002922 simulated annealing Methods 0.000 description 1
- 238000010025 steaming Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Abstract
The invention discloses an intelligent kitchen electricity self-adaptive control method and device, wherein the method comprises the following steps: determining a selected cooking task, standard cooking equipment corresponding to the cooking task and equipment labels corresponding to target cooking equipment matched with the cooking task and owned by a user; searching and determining corresponding preset scale transformation information between the target cooking equipment and the standard cooking equipment from the cloud; determining a cooking process matched with the cooking task, and differentiating the cooking process; determining the values of a plurality of cooking parameters corresponding to the standard cooking equipment in any cooking stage, and respectively performing scale transformation on the values of the plurality of cooking parameters to obtain a plurality of cooking parameters corresponding to the target cooking equipment in the cooking stage; and generating an adaptive control instruction according to the plurality of cooking parameters to control the target cooking equipment. According to the intelligent kitchen electric power cooking device, the relative adjustment of the standard cooking equipment corresponding to the cooking task can be carried out on multiple cooking parameters of the intelligent kitchen electric power.
Description
Technical Field
The invention belongs to the technical field of intelligent kitchen electric automatic control, and particularly relates to an intelligent kitchen electric self-adaptive control method and device.
Background
Intelligent cooking refers to equipment for realizing automation of a cooking process by an artificial intelligence technology, and has the functions of stir-frying, decocting, stewing, steaming, cooking and the like. The user only needs to manually throw the prepared food raw materials and seasonings into the intelligent kitchen electric appliance, and the intelligent kitchen electric appliance can realize the automatic cooking process by touching the start operation function key. The intelligent kitchen electricity can be classified from the angles of automation degree, cooking technology, dishes outlet quantity, application scene and the like.
Under the promotion of the standardized development trend of Chinese catering enterprises, the number of catering enterprises which select to use intelligent kitchen electric cooking is also continuously increased, such as fast food restaurants, restaurants and the like, and the intelligent cooking machine can ensure the dish feeding speed and the dish quality of the catering enterprises, so that the catering enterprises are facilitated to form a standardized service system. In addition, along with the continuous rising of the concepts of unmanned dining rooms, intelligent dining rooms and the like, the intelligent trend of dining is more obvious, and the intelligent kitchen electricity demand of both business and people is continuously improved.
In the current intelligent kitchen electricity field, the intelligent kitchen electricity is controlled simply through simple temperature feedback after the cooking process is set, development can be continued in the cooking process with simple setting, but along with development of internet technology, the intelligent kitchen electricity which is shared by a menu through a cloud platform starts to be out of the army, and the complexity of cooking equipment of a user under the cloud platform is determined that the menu shared by the user cannot be suitable for all cooking equipment, so that great challenges are brought to development of the intelligent kitchen electricity.
Disclosure of Invention
Accordingly, an object of the present invention is to provide an intelligent kitchen electric self-adaptive control method and apparatus, which can perform relative adjustment based on standard cooking devices corresponding to cooking tasks on a plurality of cooking parameters of the intelligent kitchen electric, so that the target cooking device can perform self-adaptive adjustment when executing cooking tasks compiled by different users sharing cloud under different cooking devices.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the invention provides an intelligent kitchen electricity self-adaptive control method, which comprises the following steps:
determining a cooking task selected by a user and standard cooking equipment corresponding to the cooking task, and determining equipment labels corresponding to target cooking equipment matched with the cooking task and owned by the user;
according to the equipment label, searching and determining corresponding preset scale conversion information between the target cooking equipment and the standard cooking equipment from the cloud;
determining a cooking process matched with the cooking task according to the cooking task, and differentiating the cooking process into a plurality of cooking stages;
determining the values of a plurality of cooking parameters corresponding to the standard cooking equipment in any cooking stage, and respectively carrying out scale transformation on the values of the plurality of cooking parameters according to the scale transformation information to obtain a plurality of cooking parameters corresponding to the target cooking equipment in the cooking stage;
and generating an adaptive control instruction according to a plurality of cooking parameters corresponding to each cooking stage of the target cooking equipment to control the target cooking equipment.
Further, the scale transformation information comprises a plurality of pieces of scale variation information of a plurality of cooking parameters required by the equipment of the type corresponding to the equipment label relative to the corresponding cooking parameters on the standard cooking equipment when the equipment of the type corresponding to the equipment label and the standard cooking equipment designated by the cooking task complete the same work target; each item of variable scale information is determined through a preset lookup table of the variable scale information, which corresponds to the model number of the part, of the equipment tag.
Further, when the cloud does not have the cooking scale information corresponding to the equipment label,
designating any cooking parameter to regulate and control the target equipment in a mode of controlling the cooking parameter variable, and simultaneously recording first state feedback data based on the cooking parameter change in the target equipment;
comparing the first state feedback data with second state feedback data corresponding to standard equipment, and determining a displacement vector between the first state feedback data and the second state feedback data;
the displacement vector is used as an adjustment vector of the cooking parameter, a first relation curve of the adjustment vector along with the change of the cooking parameter is established, and whether the displacement vector has a monotonically increasing or decreasing relation along with the change of the cooking parameter is judged according to the first relation curve;
if the first relation curve exists, the first relation curve is directly used as the variable scale information of the cooking parameter, and if the first relation curve does not exist, the average value of a plurality of adjustment vectors is used as the variable scale information of the cooking parameter.
Further, the method further comprises the step of regulating and controlling the time line of the adaptive control instruction according to the equipment delay information of the target equipment, and the steps comprise:
searching and determining corresponding preset equipment delay information from the cloud according to the equipment tag, wherein the equipment delay information is the time difference when equipment of the type corresponding to the equipment tag and standard cooking equipment designated by the cooking task reach the same working target after simultaneously starting working;
and performing phase shift regulation and control on a plurality of corresponding time points in the working parameter adjustment stage based on the equipment delay information and the generated self-adaptive control instruction.
Further, the method further comprises the steps of analyzing the evaluation score fed back by the user, and adjusting the self-adaptive control instruction according to the analysis result, wherein the steps are as follows:
step S51, obtaining a plurality of grading results of a user on any cooking task, generating a grading set, binding the grading set with an adaptive control instruction and a device label corresponding to the cooking task, generating a grading file, and uploading the grading file to a cloud for storage;
step S52, screening out scoring files with the same equipment labels and the same adaptive control instructions in the cloud after a preset time length, and carrying out average value operation of each item of scoring by using scoring sets in the scoring files to obtain a plurality of item of scoring averages to form a first scoring set;
step S53, adding disturbance behaviors to the self-adaptive control instruction, and acquiring a second grading set after the disturbance behaviors are added after a preset time length; the disturbance behavior is random value in a preset range of any cooking parameter in the self-adaptive control instruction;
step S54, respectively carrying out weighted comparison analysis on the first scoring set and the second scoring set, judging that the disturbance behavior is successful if the weighted result of the second scoring set is higher than the weighted result of the first scoring set, simultaneously continuing disturbance tests with different values according to the disturbance direction corresponding to the disturbance behavior, and taking a third scoring set and the weighted result corresponding to the third scoring set obtained after the duration of a preset time period of each disturbance test as a test result;
step S55, determining that the adaptive control instruction corresponding to the disturbance test with the largest weighted result is a local optimal adaptive control instruction according to the test result, and storing the local optimal adaptive control instruction as a new adaptive control instruction;
step S56, circularly executing the steps S53 to S55 according to the preset cycle times, and determining the remained self-adaptive control instruction after the execution is the global optimal self-adaptive control instruction.
Further, determining values of a plurality of cooking parameters corresponding to the standard cooking device in any cooking stage, and respectively performing scale transformation on the values of the plurality of cooking parameters according to the scale transformation information, wherein obtaining the plurality of cooking parameters corresponding to the target cooking device in the cooking stage comprises:
acquiring standard cooking equipment y corresponding to a cooking task and a corresponding standard cooking flow; determining a data set formed by n cooking parameters corresponding to standard cooking equipment in the t-th cooking stage according to the standard cooking flowWherein->A value representing an i-th cooking parameter at a t-th cooking stage in the cooking process;
determining scaling information between the target cooking device x and the standard cooking device y:；
wherein,expressed as cooking parameter->As a function of the argument;
for the followingIs associated with +.>The unique corresponding relation exists in one function of the target cooking equipment, and a plurality of cooking parameters corresponding to the target cooking equipment in the t-th cooking stage are calculated through the corresponding relation between the numerical value and the function.
In order to achieve the above object, the present invention also provides an intelligent kitchen electric adaptive control device, comprising:
the information acquisition module is used for determining the cooking task selected by the user and standard cooking equipment corresponding to the cooking task, and determining equipment labels corresponding to target cooking equipment matched with the cooking task and owned by the user;
the conversion information searching module is used for searching and determining corresponding preset scale conversion information between the target cooking equipment and the standard cooking equipment from the cloud according to the equipment label;
the task differentiating module is used for determining a cooking flow matched with the cooking task according to the cooking task and differentiating the cooking flow into a plurality of cooking stages;
the scale conversion module is used for determining the numerical values of a plurality of cooking parameters corresponding to the standard cooking equipment in any cooking stage, and respectively carrying out scale conversion on the numerical values of the plurality of cooking parameters according to scale conversion information to obtain a plurality of cooking parameters corresponding to the target cooking equipment in the cooking stage;
the instruction generation module is used for generating an adaptive control instruction according to a plurality of cooking parameters corresponding to each cooking stage of the target cooking equipment to control the target cooking equipment.
The invention has at least the following beneficial effects:
1. the intelligent kitchen power system can relatively adjust a plurality of cooking parameters of the intelligent kitchen power based on standard cooking equipment corresponding to the cooking task, and can enable target cooking equipment to perform self-adaptive adjustment when different users who share the cloud end execute the cooking tasks compiled by themselves under different cooking equipment.
2. The method can adapt to complex and changeable accessory conditions on the cooking equipment, and the newly produced cooking equipment can quickly determine the variable scale information of the corresponding cooking parameters according to the model of the part in the primary use process without carrying out the cooking process or the preset work of the cooking parameters.
3. The phase shift regulation and control and the time scale scaling can be carried out on a plurality of corresponding time points in the working parameter adjustment stage based on the equipment delay information and the generated self-adaptive control instruction, so that unified work of the target cooking equipment relative to the cooking flow on the standard cooking equipment is realized.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objects and other advantages of the invention may be realized and obtained by means of the instrumentalities and combinations particularly pointed out in the specification.
Drawings
In order to make the objects, technical solutions and advantageous effects of the present invention more clear, the present invention provides the following drawings for description:
FIG. 1 is a flow chart of steps of an adaptive control method for intelligent kitchen electricity in an embodiment of the invention;
FIG. 2 is a flowchart illustrating steps for adjusting an adaptive control command according to a score analysis result in an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an intelligent kitchen electric self-adaptive control device in an embodiment of the invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
The invention provides an intelligent kitchen electricity self-adaptive control method, referring to fig. 1, comprising the following steps:
step S1, determining a cooking task selected by a user and standard cooking equipment corresponding to the cooking task, and determining equipment labels corresponding to target cooking equipment matched with the cooking task and owned by the user;
step S2, searching and determining corresponding preset scale conversion information between the target cooking equipment and the standard cooking equipment from the cloud according to the equipment label;
step S3, determining a cooking process matched with the cooking task according to the cooking task, and differentiating the cooking process into a plurality of cooking stages;
step S4, determining the numerical values of a plurality of cooking parameters corresponding to the standard cooking equipment in any cooking stage, and respectively performing scale transformation on the numerical values of the plurality of cooking parameters according to the scale transformation information to obtain a plurality of cooking parameters corresponding to the target cooking equipment in the cooking stage;
and S5, generating an adaptive control instruction according to a plurality of cooking parameters corresponding to each cooking stage of the target cooking equipment to control the target cooking equipment.
The working principle and beneficial effects of the technical scheme are as follows: determining the selected cooking task and standard cooking equipment corresponding to the cooking task, and determining equipment labels corresponding to target cooking equipment matched with the cooking task and owned by a user; the user can send a cooking task to the intelligent kitchen electricity through the client, the cooking task stores relevant information in the cloud, the relevant information comprises a cooking task flow and a cooking parameter change condition at each time point, the cooking parameters are collected in the cooking process and are the internal feedback working parameters of the intelligent kitchen electricity, wherein the cooking parameters comprise, but are not limited to, direct type voltage and current parameters, indirect type parameters comprise temperature, humidity, pressure, power intensity, air pressure, air and liquid flow rates in unit time, microwave intensity, frequency and other physical parameters related to cooking; the types of the working parameters include, but are not limited to, direct type voltage and current parameters, indirect type parameters including temperature, humidity, pressure, power intensity, air pressure, flow rate of gas and liquid, air-liquid flow rate in unit time, microwave intensity and frequency and other physical parameters related to cooking; according to the equipment labels, the corresponding preset scale conversion information between the target cooking equipment and the standard cooking equipment is searched and determined from the cloud, wherein the types of the equipment labels comprise one or more of RFID labels, equipment information fields and two-dimension codes, each different equipment label corresponds to a manufacturer, equipment category, equipment subclass, equipment model, equipment size and each component source of equipment recorded with equipment corresponding to the equipment label in the cloud, each equipment label is provided with scale conversion information corresponding to the equipment label in most cases, the performance of the equipment can be adaptively adjusted to the performance of the standard cooking equipment according to the scale conversion information, and therefore the working content of the standard cooking equipment can be completely simulated when a cooking task is executed conveniently; determining a cooking process matched with a cooking task according to the cooking task, differentiating the cooking process into a plurality of cooking stages, determining the cooking process of the cooking task on standard cooking equipment after the cooking task is obtained, setting each time point in the cooking process as a dividing point if any cooking parameter changes at the time point, differentiating the time point into the plurality of cooking stages according to the change condition of the cooking process, wherein at least one cooking parameter of each cooking stage is changed compared with the corresponding cooking parameter of the last cooking stage, thereby realizing the limit differentiation of the cooking process, adaptively adjusting the equipment according to the minimum change trend, reducing the change risk caused by equipment operation faults, and improving the accuracy of adjustment work; the method comprises the steps of determining values of a plurality of cooking parameters corresponding to standard cooking equipment in any cooking stage, respectively performing scale transformation on the values of the plurality of cooking parameters according to scale transformation information to obtain a plurality of cooking parameters corresponding to target cooking equipment in the cooking stage, and finally generating a self-adaptive control instruction according to the plurality of cooking parameters corresponding to the target cooking equipment in each cooking stage to control the target cooking equipment. Through the technical scheme, the relative adjustment of the standard cooking equipment corresponding to the cooking task can be carried out on the plurality of cooking parameters of the intelligent kitchen electricity, and the target cooking equipment can be adaptively adjusted when different users who execute cloud sharing carry out the cooking tasks compiled by themselves under different cooking equipment.
In a preferred embodiment, the scale transformation information comprises a plurality of pieces of scale information of a plurality of cooking parameters required by the equipment of the type corresponding to the equipment label relative to the corresponding cooking parameters on the standard cooking equipment when the equipment of the type corresponding to the equipment label and the standard cooking equipment designated by the cooking task complete the same working objective; each item of variable scale information is determined through a preset lookup table of the variable scale information, which corresponds to the model number of the part, of the equipment tag.
The working principle and beneficial effects of the technical scheme are as follows: the scale conversion information comprises a plurality of pieces of variable scale information of a plurality of cooking parameters required by the equipment of the type corresponding to the equipment label relative to the corresponding cooking parameters on the standard cooking equipment when the equipment of the type corresponding to the equipment label and the standard cooking equipment appointed by the cooking task complete the same working target, such as voltage parameters required by heating the same volume of water in the equipment to boil in the same time length or variable scale information of temperature parameters required to be adjusted on the target equipment; each item of variable scale information can be determined through a preset part model-variable scale information lookup table corresponding to the equipment label, for example, a heating pipe B for heating exists in the target cooking equipment A, and the variable scale information between the heating pipe B and the heating pipe D on the standard cooking equipment C is searched, so that the method can adapt to complex and changeable accessory conditions on the cooking equipment, and the newly produced cooking equipment can quickly determine the variable scale information of corresponding cooking parameters according to the part model in the primary use process without carrying out a cooking process or preset work of the cooking parameters.
In a preferred embodiment, when the cloud does not have cooking scale information corresponding to the device tag,
designating any cooking parameter to regulate and control the target equipment in a mode of controlling the cooking parameter variable, and simultaneously recording first state feedback data based on the cooking parameter change in the target equipment;
comparing the state feedback data with second state feedback data corresponding to standard equipment, and determining a displacement vector between the first state feedback data and the second state feedback data;
the displacement vector is used as an adjustment vector of the cooking parameter, a first relation curve of the adjustment vector along with the change of the cooking parameter is established, and whether the displacement vector has a monotonically increasing or decreasing relation along with the change of the cooking parameter is judged according to the first relation curve;
if the first relation curve exists, the first relation curve is directly used as the variable scale information of the cooking parameter, and if the first relation curve does not exist, the average value of a plurality of adjustment vectors is used as the variable scale information of the cooking parameter.
The working principle and beneficial effects of the technical scheme are as follows: when the cloud end does not have the cooking scale information corresponding to the equipment label, namely the cloud end cannot find the corresponding equipment label and the corresponding part model, or the cooking equipment which has been aged or maintained for many years does not conform to the changing scale information corresponding to the cloud end, any cooking parameter can be designated by a mode of controlling the cooking parameter variable to regulate and control the target equipment, and meanwhile, first state feedback data based on the change of the cooking parameter in the target equipment is recorded, for example, the cooking temperature is regulated and controlled on the premise of keeping other cooking parameters unchanged, and meanwhile, the first state feedback data based on the change of the temperature parameter in the target equipment is recorded, namely the feedback temperature based on the regulation temperature change; comparing the first state feedback data with second state feedback data corresponding to standard equipment, and determining a displacement vector between the first state feedback data and the second state feedback data; using the displacement vector as an adjustment vector of the cooking parameter, establishing a first relation curve of the adjustment vector along with the change of the cooking parameter, and according to the first relation curveLinear determination of whether the displacement vector has a monotonically increasing or decreasing relationship with respect to the cooking parameter, e.g. for the same cooking parameter X, the first state feedback data has a linear relationship with the cooking parameterWhereas the second state feedback data has a linear relation to the cooking parameter>It is possible to determine the displacement vector between the first state feedback data with respect to the second state feedback data>In general, the feedback data may have slight fluctuation, and the cooking parameter on the target device may be adjusted by using the displacement vector and the cooking parameter X, so that the state feedback of the target device is consistent with the state of the standard cooking device, thereby realizing perfect reproduction of the cooking process, in the above process, the adjustment work under the condition that the target device has a fixed system error relative to the standard device is performed, and when the target device has a regular system error relative to the standard device, a monotonically increasing or decreasing relationship of the displacement vector along with the change of the cooking parameter may occur, which is specifically expressed in that the first state feedback data has a linear relationship with the cooking parameter>The second state feedback data has a linear relation to the cooking parameter +.>Determining a displacement vector +_between the first state feedback data and the second state feedback data>Wherein->And +.>Are all constants; if so, the first relation curve is directly used as the variable scale information of the cooking parameter, namelyAs a function of the change of the compensation coefficient K for adjusting the cooking parameter with the independent cooking parameter X, as the change scale information, if not, the average value of the plurality of adjustment vectors is used as the change scale information of the cooking parameter. Through the technical scheme, the determination of the variable scale information corresponding to any cooking parameter on the unknown cooking equipment is realized.
In a preferred embodiment, the method further comprises regulating the time line of the adaptive control instruction according to the device delay information of the target device, and the steps include:
searching and determining corresponding preset equipment delay information from the cloud according to the equipment tag, wherein the equipment delay information is the time difference when equipment of the type corresponding to the equipment tag and standard cooking equipment designated by the cooking task reach the same working target after simultaneously starting working;
and performing phase shift regulation and control on a plurality of corresponding time points in the working parameter adjustment stage based on the equipment delay information and the generated self-adaptive control instruction.
The working principle and beneficial effects of the technical scheme are as follows: for some working phases of different equipment, for example, a heating phase, a reaction time of 5 seconds is needed on standard cooking equipment to start heating operation, and for target cooking equipment, the heating operation can be started only by 2 seconds, which greatly influences the uniformity and the synchronism of a cooking flow, so that corresponding preset equipment delay information needs to be searched and determined from a cloud according to an equipment label, wherein the equipment delay information is the time difference between the type of equipment corresponding to the equipment label and the standard cooking equipment designated by the cooking task when the equipment reaches the same working target after the equipment label and the standard cooking equipment are simultaneously started to work; and performing phase shift regulation and control on a plurality of time points corresponding to the working parameter adjustment stage based on the equipment delay information and the generated self-adaptive control instruction, and scaling on a time scale, so that unified work of the target cooking equipment relative to the cooking flow on the standard cooking equipment is realized.
In a preferred embodiment, referring to fig. 2, the method further includes analyzing the evaluation score fed back by the user, and adjusting the adaptive control instruction according to the analysis result, which includes the following steps:
step S51, obtaining a plurality of grading results of a user on any cooking task, generating a grading set, binding the grading set with an adaptive control instruction and a device label corresponding to the cooking task, generating a grading file, and uploading the grading file to a cloud for storage;
step S52, screening out scoring files with the same equipment labels and the same adaptive control instructions in the cloud after a preset time length, and carrying out average value operation of each item of scoring by using scoring sets in the scoring files to obtain a plurality of item of scoring averages to form a first scoring set;
step S53, adding disturbance behaviors to the self-adaptive control instruction, and acquiring a second grading set after the disturbance behaviors are added after a preset time length; the disturbance behavior is random value in a preset range of any cooking parameter in the self-adaptive control instruction;
step S54, respectively carrying out weighted comparison analysis on the first scoring set and the second scoring set, judging that the disturbance behavior is successful if the weighted result of the second scoring set is higher than the weighted result of the first scoring set, simultaneously continuing disturbance tests with different values according to the disturbance direction corresponding to the disturbance behavior, and taking a third scoring set and the weighted result corresponding to the third scoring set obtained after the duration of a preset time period of each disturbance test as a test result;
step S55, determining that the adaptive control instruction corresponding to the disturbance test with the largest weighted result is a local optimal adaptive control instruction according to the test result, and storing the local optimal adaptive control instruction as a new adaptive control instruction;
step S56, circularly executing the steps S3 to S5 according to the preset cycle times, and determining the self-adaptive control instruction left after the execution is completed as the global optimal self-adaptive control instruction.
The working principle and beneficial effects of the technical scheme are as follows: in the cooking process, the cooking flow can be optimized according to the score of the user on the cooking task, in the embodiment, the optimization thought of the simulated annealing algorithm is adopted, the disturbance behavior is applied to the adaptive control instruction under the standard condition, for example, the temperature parameter of one or more stages in the cooking flow is improved or reduced in a proper range, whether the disturbance achieves a better cooking effect or not is determined according to the feedback of the user, and therefore the determination of the globally optimal adaptive control instruction is finally achieved. Step S51, a plurality of grading results of a user on any cooking task are obtained, a grading set is generated, the grading set is bound with an adaptive control instruction and an equipment label corresponding to the cooking task, and then a grading file is generated and uploaded to a cloud for storage, wherein the grading items include but are not limited to taste, viscosity, fragrance, color and the like; step S52, screening out scoring files with the same equipment labels and the same adaptive control instructions in the cloud after a preset time length, and carrying out average value operation of each item of scoring by using scoring sets in the scoring files to obtain a plurality of item of scoring averages to form a first scoring set; step S53, adding disturbance behaviors to the self-adaptive control instruction, and acquiring a second grading set after the disturbance behaviors are added after a preset time length; the disturbance behavior is random value in a preset range of any cooking parameter in the self-adaptive control instruction; step S54, respectively carrying out weighted comparison analysis on the first scoring set and the second scoring set, judging that the disturbance behavior is successful if the weighted result of the second scoring set is higher than the weighted result of the first scoring set, wherein the weighted purpose is to determine the priority degree of the scoring item, the weighted coefficient can be preset, simultaneously, carrying out disturbance tests with different values according to the disturbance direction corresponding to the disturbance behavior, and taking a third scoring set obtained after each disturbance test lasts for a preset time length and the weighted result corresponding to the third scoring set as a test result, for example, after the added disturbance behavior (the temperature parameter in the cooking stage 8 is adjusted by 5 degrees) is successful, carrying out disturbance tests with different values according to the disturbance direction corresponding to the disturbance behavior, namely carrying out multiple disturbance tests under the condition of 3-7 degrees of temperature adjustment respectively; step S55, determining that the adaptive control instruction corresponding to the disturbance test with the largest weighted result is a local optimal adaptive control instruction according to the test result, and storing the adaptive control instruction as a new adaptive control instruction, thereby realizing the generation of the local optimal adaptive control instruction; step S56, circularly executing the steps S53 to S55 according to the preset circulation times, determining the self-adaptive control instruction remained after the execution is the global optimal self-adaptive control instruction, and converging the disturbance by setting constraint conditions to obtain a stable solution. By the technical scheme, the determination of the globally optimal self-adaptive control instruction of the cooking equipment corresponding to the equipment label is realized.
In a preferred embodiment, determining the values of a plurality of cooking parameters corresponding to the standard cooking device in any one cooking stage, and respectively performing scale transformation on the values of the plurality of cooking parameters according to the scale transformation information, so as to obtain the plurality of cooking parameters corresponding to the target cooking device in the cooking stage, wherein the steps include:
acquiring standard cooking equipment y corresponding to a cooking task and a corresponding standard cooking flow; determining a data set formed by n cooking parameters corresponding to standard cooking equipment in the t-th cooking stage according to the standard cooking flowWherein->A value representing an i-th cooking parameter at a t-th cooking stage in the cooking process;
determining scaling information between the target cooking device x and the standard cooking device y:；
wherein,expressed as cooking parameter->As a function of the argument;
for the followingIs associated with +.>The unique corresponding relation exists in one function of the target cooking equipment, and a plurality of cooking parameters corresponding to the target cooking equipment in the t-th cooking stage are calculated through the corresponding relation between the numerical value and the function.
The working principle and beneficial effects of the technical scheme are as follows: when the scale transformation of the cooking parameters is carried out, standard cooking equipment y corresponding to the cooking task and a corresponding standard cooking flow are obtained; determining a data set formed by n cooking parameters corresponding to standard cooking equipment in the t-th cooking stage according to the standard cooking flowWherein->A value representing an i-th cooking parameter at a t-th cooking stage in the cooking process; determining scaling information between the target cooking device x and the standard cooking device y:the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Expressed as cooking parameter->A function which is an independent variable, wherein the function is n in total and corresponds to n cooking parameters respectively; for->Is associated with +.>The unique corresponding relation exists in one function of the target cooking equipment, and a plurality of cooking parameters corresponding to the target cooking equipment in the t-th cooking stage are calculated through the corresponding relation between the numerical value and the function. Therefore, the numerical values of the plurality of cooking parameters are respectively subjected to scale transformation according to the scale transformation information, and the plurality of cooking parameters corresponding to the target cooking equipment in the cooking stage are obtained.
In order to achieve the above object, the present invention further provides an intelligent kitchen electric adaptive control device, referring to fig. 3, including:
the information acquisition module is used for determining the cooking task selected by the user and standard cooking equipment corresponding to the cooking task, and determining equipment labels corresponding to target cooking equipment matched with the cooking task and owned by the user;
the conversion information searching module is used for searching and determining corresponding preset scale conversion information between the target cooking equipment and the standard cooking equipment from the cloud according to the equipment label;
the task differentiating module is used for determining a cooking flow matched with the cooking task according to the cooking task and differentiating the cooking flow into a plurality of cooking stages;
the scale conversion module is used for determining the numerical values of a plurality of cooking parameters corresponding to the standard cooking equipment in any cooking stage, and respectively carrying out scale conversion on the numerical values of the plurality of cooking parameters according to scale conversion information to obtain a plurality of cooking parameters corresponding to the target cooking equipment in the cooking stage;
the instruction generation module is used for generating an adaptive control instruction according to a plurality of cooking parameters corresponding to each cooking stage of the target cooking equipment to control the target cooking equipment.
The working principle and beneficial effects of the technical scheme are as follows: determining the selected cooking task and standard cooking equipment corresponding to the cooking task through an information acquisition module, and determining equipment labels corresponding to target cooking equipment matched with the cooking task and owned by a user; the user can send a cooking task to the intelligent kitchen electricity through the client, the cooking task stores relevant information in the cloud, the relevant information comprises a cooking task flow and a cooking parameter change condition at each time point, the cooking parameters are collected in the cooking process and are the internal feedback working parameters of the intelligent kitchen electricity, wherein the cooking parameters comprise, but are not limited to, direct type voltage and current parameters, indirect type parameters comprise temperature, humidity, pressure, power intensity, air pressure, air and liquid flow rates in unit time, microwave intensity, frequency and other physical parameters related to cooking; the types of the working parameters include, but are not limited to, direct type voltage and current parameters, indirect type parameters including temperature, humidity, pressure, power intensity, air pressure, flow rate of gas and liquid, air-liquid flow rate in unit time, microwave intensity and frequency and other physical parameters related to cooking; the method comprises the steps that a transformation information searching module searches and determines corresponding preset scale transformation information between target cooking equipment and standard cooking equipment from a cloud according to equipment labels, wherein the types of the equipment labels comprise one or more of RFID labels, equipment information fields and two-dimensional codes, each different equipment label corresponds to a manufacturer, equipment category, equipment subclass, equipment model, equipment size and various component sources of equipment recorded with equipment corresponding to the equipment label in the cloud, each equipment label is provided with scale transformation information corresponding to the equipment label under most conditions, and the performance of the equipment can be adaptively adjusted to the performance of the standard cooking equipment according to the scale transformation information, so that the working content of the standard cooking equipment can be completely simulated when a cooking task is executed conveniently; determining a cooking process matched with a cooking task through a task differentiating module according to the cooking task, differentiating the cooking process into a plurality of cooking stages, determining the cooking process of the cooking task on standard cooking equipment after the cooking task is obtained, setting each time point in the cooking process as a dividing point if any cooking parameter changes at the time point, differentiating the time point into a plurality of cooking stages according to the change condition of the cooking process, wherein at least one cooking parameter of each cooking stage is changed compared with the corresponding cooking parameter of the last cooking stage, thereby realizing the limit differentiation of the cooking process, adaptively adjusting the equipment according to the minimum change trend, reducing the change risk caused by equipment operation faults, and improving the accuracy of adjustment work; the method comprises the steps of determining the numerical values of a plurality of cooking parameters corresponding to standard cooking equipment in any cooking stage through a scale conversion module, respectively performing scale conversion on the numerical values of the plurality of cooking parameters according to scale conversion information to obtain a plurality of cooking parameters corresponding to target cooking equipment in the cooking stage, and finally generating a self-adaptive control instruction to control the target cooking equipment through an instruction generation module according to the plurality of cooking parameters corresponding to the target cooking equipment in each cooking stage. Through the technical scheme, the relative adjustment of the standard cooking equipment corresponding to the cooking task can be carried out on the plurality of cooking parameters of the intelligent kitchen electricity, and the target cooking equipment can be adaptively adjusted when different users who execute cloud sharing carry out the cooking tasks compiled by themselves under different cooking equipment.
Finally, it is noted that the above-mentioned preferred embodiments are only intended to illustrate rather than limit the invention, and that, although the invention has been described in detail by means of the above-mentioned preferred embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the scope of the invention as defined by the appended claims.
Claims (7)
1. An intelligent kitchen electricity self-adaptive control method is characterized by comprising the following steps:
determining a cooking task selected by a user and standard cooking equipment corresponding to the cooking task, and determining equipment labels corresponding to target cooking equipment matched with the cooking task and owned by the user;
according to the equipment label, searching and determining corresponding preset scale conversion information between the target cooking equipment and the standard cooking equipment from the cloud;
determining a cooking process matched with the cooking task according to the cooking task, and differentiating the cooking process into a plurality of cooking stages;
determining the values of a plurality of cooking parameters corresponding to the standard cooking equipment in any cooking stage, and respectively carrying out scale transformation on the values of the plurality of cooking parameters according to the scale transformation information to obtain a plurality of cooking parameters corresponding to the target cooking equipment in the cooking stage;
and generating an adaptive control instruction according to a plurality of cooking parameters corresponding to each cooking stage of the target cooking equipment to control the target cooking equipment.
2. The adaptive control method of intelligent kitchen electricity according to claim 1, wherein the scale transformation information includes a plurality of variable scale information of a plurality of cooking parameters required by a device of a type corresponding to the device tag relative to a corresponding cooking parameter on a standard cooking device when the device of the type corresponding to the device tag and the standard cooking device designated by the cooking task complete the same work objective; each item of variable scale information is searched and determined through a preset equipment label corresponding part model-variable scale information lookup table.
3. The method of claim 1, wherein when the cloud end does not have cooking scale information corresponding to the device tag,
designating any cooking parameter to regulate and control the target equipment in a mode of controlling the cooking parameter variable, and simultaneously recording first state feedback data based on the cooking parameter change in the target equipment;
comparing the first state feedback data with second state feedback data corresponding to standard equipment, and determining a displacement vector between the first state feedback data and the second state feedback data;
the displacement vector is used as an adjustment vector of the cooking parameter, a first relation curve of the adjustment vector along with the change of the cooking parameter is established, and whether the displacement vector has a monotonically increasing or decreasing relation along with the change of the cooking parameter is judged according to the first relation curve;
if the first relation curve exists, the first relation curve is directly used as the variable scale information of the cooking parameter, and if the first relation curve does not exist, the average value of a plurality of adjustment vectors is used as the variable scale information of the cooking parameter.
4. The intelligent kitchen electricity self-adaptive control method according to claim 1, further comprising the step of regulating a time line of the self-adaptive control command according to device delay information of the target device, the step comprising:
searching and determining corresponding preset equipment delay information from the cloud according to the equipment tag, wherein the equipment delay information is the time difference when equipment of the type corresponding to the equipment tag and standard cooking equipment designated by the cooking task reach the same working target after simultaneously starting working;
and performing phase shift regulation and control on a plurality of corresponding time points in the working parameter adjustment stage based on the equipment delay information and the generated self-adaptive control instruction.
5. The intelligent kitchen electricity self-adaptive control method according to claim 1, further comprising the steps of analyzing the evaluation score fed back by the user, and adjusting the self-adaptive control command according to the analysis result, wherein the steps are as follows:
step S51, obtaining a plurality of grading results of a user on any cooking task, generating a grading set, binding the grading set with an adaptive control instruction and a device label corresponding to the cooking task, generating a grading file, and uploading the grading file to a cloud for storage;
step S52, screening out scoring files with the same equipment labels and the same adaptive control instructions in the cloud after a preset time length, and carrying out average value operation of each item of scoring by using scoring sets in the scoring files to obtain a plurality of item of scoring averages to form a first scoring set;
step S53, adding disturbance behaviors to the self-adaptive control instruction, and acquiring a second grading set after the disturbance behaviors are added after a preset time length; the disturbance behavior is random value in a preset range of any cooking parameter in the self-adaptive control instruction;
step S54, respectively carrying out weighted comparison analysis on the first scoring set and the second scoring set, judging that the disturbance behavior is successful if the weighted result of the second scoring set is higher than the weighted result of the first scoring set, simultaneously continuing disturbance tests with different values according to the disturbance direction corresponding to the disturbance behavior, and taking a third scoring set and the weighted result corresponding to the third scoring set obtained after the duration of a preset time period of each disturbance test as a test result;
step S55, determining that the adaptive control instruction corresponding to the disturbance test with the largest weighted result is a local optimal adaptive control instruction according to the test result, and storing the local optimal adaptive control instruction as a new adaptive control instruction;
step S56, circularly executing the steps S53 to S55 according to the preset cycle times, and determining the remained self-adaptive control instruction after the execution is the global optimal self-adaptive control instruction.
6. The method of claim 1, wherein determining the values of the plurality of cooking parameters corresponding to the standard cooking device in any one cooking stage, and performing scale transformation on the values of the plurality of cooking parameters according to the scale transformation information, respectively, to obtain the plurality of cooking parameters corresponding to the target cooking device in the cooking stage comprises:
acquiring standard cooking equipment y corresponding to a cooking task and a corresponding standard cooking flow; determining a data set formed by n cooking parameters corresponding to standard cooking equipment in the t-th cooking stage according to the standard cooking flowWherein->A value representing an i-th cooking parameter at a t-th cooking stage in the cooking process;
determining scaling information between the target cooking device x and the standard cooking device y:；
wherein,expressed as cooking parameter->As a function of the argument;
for the followingIs associated with +.>The unique corresponding relation exists in one function of the target cooking equipment, and a plurality of cooking parameters corresponding to the target cooking equipment in the t-th cooking stage are calculated through the corresponding relation between the numerical value and the function.
7. An intelligent kitchen electricity self-adaptive control device, which is characterized by comprising:
the information acquisition module is used for determining the cooking task selected by the user and standard cooking equipment corresponding to the cooking task, and determining equipment labels corresponding to target cooking equipment matched with the cooking task and owned by the user;
the conversion information searching module is used for searching and determining corresponding preset scale conversion information between the target cooking equipment and the standard cooking equipment from the cloud according to the equipment label;
the task differentiating module is used for determining a cooking flow matched with the cooking task according to the cooking task and differentiating the cooking flow into a plurality of cooking stages;
the scale conversion module is used for determining the numerical values of a plurality of cooking parameters corresponding to the standard cooking equipment in any cooking stage, and respectively carrying out scale conversion on the numerical values of the plurality of cooking parameters according to scale conversion information to obtain a plurality of cooking parameters corresponding to the target cooking equipment in the cooking stage;
the instruction generation module is used for generating an adaptive control instruction according to a plurality of cooking parameters corresponding to each cooking stage of the target cooking equipment to control the target cooking equipment.
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